# Characterizing Tumor Heterogeneity With Functional Imaging and Quantifying High-Risk Tumor Volume for Early Prediction of Treatment Outcome: Cervical Cancer as a Model

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/characterizing-tumor-heterogeneity-with-functional-imaging-and-quantifying-high/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Nina A. Mayr,Zhibin Huang,Jian Z. Wang,Simon S. Lo,Joline M. Fan,J.C. Grecula,Steffen Sammet,Christina L. Sammet,Guang Jia,Jun Zhang,Michael V. Knopp,William T. C. Yuh |
| Citations | 75 |
| DOI | 10.1016/j.ijrobp.2011.08.011 |
| Fields | Medicine |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W2068011562 |
| PMID | 22208967 |
| Type | article |
| Year | 2011 |

## Paper authors

- [Zhibin Huang](https://scholariq.org/researchers/zhibin-huang/)

## Paper journal

- [International Journal of Radiation Oncology*Biology*Physics](https://scholariq.org/journals/international-journal-of-radiation-oncology-biology-physics/)

## Paper primary topic

- [MRI in cancer diagnosis](https://scholariq.org/topics/mri-in-cancer-diagnosis/)

## Paper topics

- [MRI in cancer diagnosis](https://scholariq.org/topics/mri-in-cancer-diagnosis/)
- [Endometrial and Cervical Cancer Treatments](https://scholariq.org/topics/endometrial-and-cervical-cancer-treatments/)
- [Radiomics and Machine Learning in Medical Imaging](https://scholariq.org/topics/radiomics-and-machine-learning-in-medical-imaging/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.